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Keywords = digital twins

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39 pages, 5251 KiB  
Article
Metamodeling Approach to Sociotechnical Systems’ External Context Digital Twins Building: A Higher Education Case Study
by Ana Perisic, Ines Perisic, Marko Lazic and Branko Perisic
Appl. Sci. 2025, 15(15), 8708; https://doi.org/10.3390/app15158708 (registering DOI) - 6 Aug 2025
Abstract
Sociotechnical systems (STSs) are generally assumed to be systems that incorporate humans and technology, strongly depending on a sustainable equilibrium between the following nondeterministic social context ingredients: social structures, roles, and rights, as well as the designers’ Holy Grail, the deterministic nature of [...] Read more.
Sociotechnical systems (STSs) are generally assumed to be systems that incorporate humans and technology, strongly depending on a sustainable equilibrium between the following nondeterministic social context ingredients: social structures, roles, and rights, as well as the designers’ Holy Grail, the deterministic nature of the underlying technical system. The fact that the relevant social concepts are more mature than the supporting technologies qualifies the digital transformation of sociotechnical systems as a reengineering rather than an engineering endeavor. Preserving the social mission throughout the digital transformation process in varying social contexts is mandatory, making the digital twins (DT) methodology application a contemporary research hotspot. In this research, we combined continuous transformation STS theory principles, an observer-based system-of-sociotechnical-systems (SoSTS) architecture model, and digital twinning methods to address common STS context representation challenges. Additionally, based on model-driven systems engineering methodology and meta-object-facility principles, the research specifies the universal meta-concepts and meta-modeling templates, supporting the creation of arbitrary sociotechnical systems’ external context digital twins. Due to the inherent diversity, significantly influenced by geopolitical, economic, and cultural influencers, a higher education external context specialization illustrates the reusability potentials of the proposed universal meta-concepts. Substituting higher-education-related meta-concepts and meta-models with arbitrary domain-dependent specializations further fosters the proposed universal meta-concepts’ reusability. Full article
21 pages, 5215 KiB  
Article
A Cyber-Physical Integrated Framework for Developing Smart Operations in Robotic Applications
by Tien-Lun Liu, Po-Chun Chen, Yi-Hsiang Chao and Kuan-Chun Huang
Electronics 2025, 14(15), 3130; https://doi.org/10.3390/electronics14153130 - 6 Aug 2025
Abstract
The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues [...] Read more.
The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues of intelligent robots with cognitive and coordination capability by introducing cyber-physical integration technology. The authors propose a system architecture with open-source software and low-cost hardware based on the 5C hierarchy and then conduct experiments to verify the proposed framework. These experiments involve the collection of real-time data using a depth camera, object detection to recognize obstacles, simulation of collision avoidance for a robotic arm, and cyber-physical integration to perform a robotic task. The proposed framework realizes the scheme of the 5C architecture of Industry 4.0 and establishes a digital twin in cyberspace. By utilizing connection, conversion, calculation, simulation, verification, and operation, the robotic arm is capable of making independent judgments and appropriate decisions to successfully complete the assigned task, thereby verifying the proposed framework. Such a cyber-physical integration system is characterized by low cost but good effectiveness. Full article
(This article belongs to the Topic Innovation, Communication and Engineering)
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29 pages, 3542 KiB  
Review
Digital Twins, AI, and Cybersecurity in Additive Manufacturing: A Comprehensive Review of Current Trends and Challenges
by Md Sazol Ahmmed, Laraib Khan, Muhammad Arif Mahmood and Frank Liou
Machines 2025, 13(8), 691; https://doi.org/10.3390/machines13080691 - 6 Aug 2025
Abstract
The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their [...] Read more.
The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article
(This article belongs to the Special Issue Neural Networks Applied in Manufacturing and Design)
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36 pages, 1832 KiB  
Review
Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
by Mohammad Abidur Rahman, Md Farhan Shahrior, Kamran Iqbal and Ali A. Abushaiba
Automation 2025, 6(3), 37; https://doi.org/10.3390/automation6030037 - 5 Aug 2025
Abstract
The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly [...] Read more.
The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article
(This article belongs to the Section Industrial Automation and Process Control)
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35 pages, 6795 KiB  
Article
Thermal Analysis of Energy Efficiency Performance and Indoor Comfort in a LEED-Certified Campus Building in the United Arab Emirates
by Khushbu Mankani, Mutasim Nour and Hassam Nasarullah Chaudhry
Energies 2025, 18(15), 4155; https://doi.org/10.3390/en18154155 - 5 Aug 2025
Abstract
Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green [...] Read more.
Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Performance in Buildings)
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51 pages, 4099 KiB  
Review
Artificial Intelligence and Digital Twin Technologies for Intelligent Lithium-Ion Battery Management Systems: A Comprehensive Review of State Estimation, Lifecycle Optimization, and Cloud-Edge Integration
by Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Hicham Chaoui, Saad Mekhilef, Shi Xue Dou and Khay See
Batteries 2025, 11(8), 298; https://doi.org/10.3390/batteries11080298 - 5 Aug 2025
Abstract
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery [...] Read more.
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article
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30 pages, 9435 KiB  
Article
Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion
by Tiantian Xu, Xuedong Zhang and Wenlei Sun
Appl. Sci. 2025, 15(15), 8655; https://doi.org/10.3390/app15158655 (registering DOI) - 5 Aug 2025
Abstract
To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising [...] Read more.
To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. Full article
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22 pages, 5322 KiB  
Article
Comparative Modeling of Vanadium Redox Flow Batteries Using Multiple Linear Regression and Random Forest Algorithms
by Ammar Ali, Sohel Anwar and Afshin Izadian
Energy Storage Appl. 2025, 2(3), 11; https://doi.org/10.3390/esa2030011 - 5 Aug 2025
Abstract
This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model [...] Read more.
This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications. Full article
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28 pages, 14684 KiB  
Article
SDT4Solar: A Spatial Digital Twin Framework for Scalable Rooftop PV Planning in Urban Environments
by Athenee Teofilo, Qian (Chayn) Sun and Marco Amati
Smart Cities 2025, 8(4), 128; https://doi.org/10.3390/smartcities8040128 - 4 Aug 2025
Abstract
To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. [...] Read more.
To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article
(This article belongs to the Topic Sustainable Building Development and Promotion)
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22 pages, 4426 KiB  
Article
A Digital Twin Platform for Real-Time Intersection Traffic Monitoring, Performance Evaluation, and Calibration
by Abolfazl Afshari, Joyoung Lee and Dejan Besenski
Infrastructures 2025, 10(8), 204; https://doi.org/10.3390/infrastructures10080204 - 4 Aug 2025
Abstract
Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with [...] Read more.
Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article
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27 pages, 427 KiB  
Article
ROS-Compatible Robotics Simulators for Industry 4.0 and Industry 5.0: A Systematic Review of Trends and Technologies
by Jose M. Flores Gonzalez, Enrique Coronado and Natsuki Yamanobe
Appl. Sci. 2025, 15(15), 8637; https://doi.org/10.3390/app15158637 (registering DOI) - 4 Aug 2025
Abstract
Simulators play a critical role in the development and testing of Industry 4.0 and Industry 5.0 applications. However, few studies have examined their capabilities beyond physics modeling, particularly in terms of connectivity and integration within broader robotic ecosystems. This review addresses this gap [...] Read more.
Simulators play a critical role in the development and testing of Industry 4.0 and Industry 5.0 applications. However, few studies have examined their capabilities beyond physics modeling, particularly in terms of connectivity and integration within broader robotic ecosystems. This review addresses this gap by focusing on ROS-compatible simulators. Using the SEGRESS methodology in combination with the PICOC framework, this study systematically analyzes 65 peer-reviewed articles published between 2021 and 2025 to identify key trends, capabilities, and application domains of ROS-integrated robotic simulators in industrial and manufacturing contexts. Our findings indicate that Gazebo is the most commonly used simulator in Industry 4.0, primarily due to its strong compatibility with ROS, while Unity is most prevalent in Industry 5.0 for its advanced visualization, support for human interaction, and extended reality (XR) features. Additionally, the study examines the adoption of ROS and ROS 2, and identifies complementary communication and integration technologies that help address the current interoperability challenges of ROS. These insights are intended to inform researchers and practitioners about the current landscape of simulation platforms and the core technologies frequently incorporated into robotics research. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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38 pages, 2159 KiB  
Review
Leveraging Big Data and AI for Sustainable Urban Mobility Solutions
by Oluwaleke Yusuf, Adil Rasheed and Frank Lindseth
Urban Sci. 2025, 9(8), 301; https://doi.org/10.3390/urbansci9080301 - 4 Aug 2025
Viewed by 11
Abstract
Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts [...] Read more.
Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article
(This article belongs to the Special Issue Sustainable Urbanization, Regional Planning and Development)
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18 pages, 728 KiB  
Article
Digital Twins and Cross-Border Logistics Systems Risk Management Capability: An Innovation Diffusion Perspective
by Shuyan Li, Pengwei Jin, Saier Su, Jinge Yao and Qiwei Pang
Systems 2025, 13(8), 658; https://doi.org/10.3390/systems13080658 - 4 Aug 2025
Viewed by 25
Abstract
This study ground in the Innovation Diffusion Theory (IDT), explores the value of digital twin technology in cross-border logistics risk management. Using structural equation modeling, it examines how five innovation characteristics of digital twins—relative advantage, compatibility, complexity, trialability, and observability—influence risk management capabilities, [...] Read more.
This study ground in the Innovation Diffusion Theory (IDT), explores the value of digital twin technology in cross-border logistics risk management. Using structural equation modeling, it examines how five innovation characteristics of digital twins—relative advantage, compatibility, complexity, trialability, and observability—influence risk management capabilities, specifically robustness and resilience, within cross-border logistics systems. The findings reveal that relative advantage, compatibility, trialability, and observability significantly enhance both robustness and resilience, while complexity does not show a significant negative impact. Furthermore, the study confirms that improvements in risk management capabilities contribute positively to competitive performance. This research not only enriches the theoretical understanding of digital twin applications in cross-border logistics but also offers valuable insights for practical implementation by enterprises. Full article
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13 pages, 2517 KiB  
Article
A Framework for the Dynamic Mapping of Precipitations Using Open-Source 3D WebGIS Technology
by Marcello La Guardia, Antonio Angrisano and Giuseppe Mussumeci
Geographies 2025, 5(3), 40; https://doi.org/10.3390/geographies5030040 - 4 Aug 2025
Viewed by 47
Abstract
Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts [...] Read more.
Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article
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25 pages, 2807 KiB  
Article
Drivers of Population Dynamics in High-Altitude Counties of Sichuan Province, China
by Xiangyu Dong, Mengge Du and Shichen Zhao
Sustainability 2025, 17(15), 7051; https://doi.org/10.3390/su17157051 - 4 Aug 2025
Viewed by 68
Abstract
The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous [...] Read more.
The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking. Full article
(This article belongs to the Special Issue Advanced Studies in Sustainable Urban Planning and Urban Development)
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